Automatic speech recognition with a cochlear implant front-end

2007 ◽  
Author(s):  
Waldo Nogueira ◽  
Tamás Harczos ◽  
Bernd Edler ◽  
Jörn Ostermann ◽  
Andreas Büchner
Keyword(s):  
Speech Recognition ◽  
Cochlear Implant ◽  
Automatic Speech Recognition ◽  
Front End

scholarly journals Development of Intelligent Terminal-based Signal Acquisition System for Cochlear Implant

2018 ◽  
Vol 232 ◽  
pp. 01015 ◽  
Author(s):  
Yousheng Chen ◽  
Weifang Chen
Keyword(s):  
Speech Recognition ◽  
Cochlear Implant ◽  
Acquisition System ◽  
Signal Acquisition ◽  
Noisy Environment ◽  
System Monitoring ◽  
Intelligent Monitoring ◽  
Intelligent Terminal ◽  
Front End ◽  
Mismatch Condition

Present speech recognition of cochlear implant is still low for situation of noisy environment or under mismatch condition, and more researches focus on improving front-end signal acquisition and speech recognition. To simplify signal acquisition and algorithm research, we develop an intelligent terminal-based signal acquisition system for cochlear implant, in which the electric relay and many sensors are adopted to implement system monitoring function. The proposed system platform is helpful to actualize algorithm research and intelligent monitoring, adding to its value of further research of speech recognition improvement.

Performance Analysis of various Front-end and Back End Amalgamations for Noise-robust DNN-based ASR

2020 ◽  
Vol 13 ◽  
Author(s):  
Mohit Dua ◽  
Pawandeep Singh Sethi ◽  
Vinam Agrawal ◽  
Raghav Chawla
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Gaussian Mixture ◽  
Performance Comparison ◽  
Acoustic Modeling ◽  
Extraction Techniques ◽  
Front End ◽  
Noise Robust ◽  
Asr System

Introduction: An Automatic Speech Recognition (ASR) system enables to recognize the speech utterances and thus can be used to convert speech into text for various purposes. These systems are deployed in different environments such as clean or noisy and are used by all ages or types of people. These also present some of the major difficulties faced in the development of an ASR system. Thus, an ASR system need to be efficient, while also being accurate and robust. Our main goal is to minimize the error rate during training as well as testing phases, while implementing an ASR system. Performance of ASR depends upon different combinations of feature extraction techniques and back-end techniques. In this paper, using a continuous speech recognition system, the performance comparison of different combinations of feature extraction techniques and various types of back-end techniques has been presented Methods: Hidden Markov Models (HMMs), Subspace Gaussian Mixture Models (SGMMs) and Deep Neural Networks (DNNs) with DNN-HMM architecture, namely Karel's, Dan's and Hybrid DNN-SGMM architecture are used at the back-end of the implemented system. Mel frequency Cepstral Coefficient (MFCC), Perceptual Linear Prediction (PLP), and Gammatone Frequency Cepstral coefficients (GFCC) are used as feature extraction techniques at the front-end of the proposed system. Kaldi toolkit has been used for the implementation of the proposed work. The system is trained on the Texas Instruments-Massachusetts Institute of Technology (TIMIT) speech corpus for English language Results: The experimental results show that MFCC outperforms GFCC and PLP in noiseless conditions, while PLP tends to outperform MFCC and GFCC in noisy conditions. Furthermore, the hybrid of Dan's DNN implementation along with SGMM performs the best for the back-end acoustic modeling. The proposed architecture with PLP feature extraction technique in the front end and hybrid of Dan's DNN implementation along with SGMM at the back end outperforms the other combinations in a noisy environment. Conclusion: Automatic Speech recognition has numerous applications in our lives like Home automation, Personal assistant, Robotics etc. It is highly desirable to build an ASR system with good performance. The performance Automatic Speech Recognition is affected by various factors which include vocabulary size, whether system is speaker dependent or independent, whether speech is isolated, discontinuous or continuous, adverse conditions like noise. The paper presented an ensemble architecture that uses PLP for feature extraction at the front end and a hybrid of SGMM + Dan's DNN in the backend to build a noise robust ASR system Discussion: The presented work in this paper discusses the performance comparison of continuous ASR systems developed using different combinations of front-end feature extraction (MFCC, PLP, and GFCC) and back-end acoustic modeling (mono-phone, tri-phone, SGMM, DNN and hybrid DNN-SGMM) techniques. Each type of front-end technique is tested in combination with each type of back-end technique. Finally, it compares the results of the combinations thus formed, to find out the best performing combination in noisy and clean conditions

Multichannel Audio Front-End for Far-Field Automatic Speech Recognition

2018 ◽  
Author(s):  
Amit Chhetri ◽  
Philip Hilmes ◽  
Trausti Kristjansson ◽  
Wai Chu ◽  
Mohamed Mansour ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Far Field ◽  
Multichannel Audio ◽  
Front End
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